HongChuang/ultralytics-ascend
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity Actions 3

Update YOLO11 Actions and Docs (#16596)

Browse source 
Signed-off-by: UltralyticsAssistant <web@ultralytics.com>
This commit is contained in:
Ultralytics Assistant 2024-10-01 16:58:12 +02:00 • committed by GitHub
parent 51e93d6111
commit 97f38409fb
No known key found for this signature in database
GPG key ID: B5690EEEBB952194
124 changed files with 1948 additions and 1948 deletions
Download patch file Download diff file Expand all files Collapse all files

14
docs/en/datasets/detect/globalwheat2020.md
View file

@ -38,7 +38,7 @@ A YAML (Yet Another Markup Language) file is used to define the dataset configur
## Usage
To train a YOLOv8n model on the Global Wheat Head Dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
To train a YOLO11n model on the Global Wheat Head Dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
!!! example "Train Example"
@ -48,7 +48,7 @@ To train a YOLOv8n model on the Global Wheat Head Dataset for 100 [epochs](https
from ultralytics import YOLO
# Load a model
model = YOLO("yolov8n.pt") # load a pretrained model (recommended for training)
model = YOLO("yolo11n.pt") # load a pretrained model (recommended for training)
# Train the model
results = model.train(data="GlobalWheat2020.yaml", epochs=100, imgsz=640)
@ -58,7 +58,7 @@ To train a YOLOv8n model on the Global Wheat Head Dataset for 100 [epochs](https
```bash
# Start training from a pretrained *.pt model
yolo detect train data=GlobalWheat2020.yaml model=yolov8n.pt epochs=100 imgsz=640
yolo detect train data=GlobalWheat2020.yaml model=yolo11n.pt epochs=100 imgsz=640
```
## Sample Data and Annotations
@ -96,9 +96,9 @@ We would like to acknowledge the researchers and institutions that contributed t
The Global Wheat Head Dataset is primarily used for developing and training deep learning models aimed at wheat head detection. This is crucial for applications in wheat phenotyping and crop management, allowing for more accurate estimations of wheat head density, size, and overall crop yield potential. Accurate detection methods help in assessing crop health and maturity, essential for efficient crop management.
### How do I train a YOLOv8n model on the Global Wheat Head Dataset?
### How do I train a YOLO11n model on the Global Wheat Head Dataset?
To train a YOLOv8n model on the Global Wheat Head Dataset, you can use the following code snippets. Make sure you have the `GlobalWheat2020.yaml` configuration file specifying dataset paths and classes:
To train a YOLO11n model on the Global Wheat Head Dataset, you can use the following code snippets. Make sure you have the `GlobalWheat2020.yaml` configuration file specifying dataset paths and classes:
!!! example "Train Example"
@ -108,7 +108,7 @@ To train a YOLOv8n model on the Global Wheat Head Dataset, you can use the follo
from ultralytics import YOLO
# Load a pre-trained model (recommended for training)
model = YOLO("yolov8n.pt")
model = YOLO("yolo11n.pt")
# Train the model
results = model.train(data="GlobalWheat2020.yaml", epochs=100, imgsz=640)
@ -118,7 +118,7 @@ To train a YOLOv8n model on the Global Wheat Head Dataset, you can use the follo
```bash
# Start training from a pretrained *.pt model
yolo detect train data=GlobalWheat2020.yaml model=yolov8n.pt epochs=100 imgsz=640
yolo detect train data=GlobalWheat2020.yaml model=yolo11n.pt epochs=100 imgsz=640
```
For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.